IdentityByDescentDispersal.jl: Inferring dispersal rates with identity-by-descent blocks
IdentityByDescentDispersal.jl: Inferring dispersal rates with identity-by-descent blocks - Published in JOSS (2026)
Science Score: 92.0%
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Published in Journal of Open Source Software
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Repository
Efficient estimation of effective densities and dispersal rates using identity-by-descent blocks
Basic Info
- Host: GitHub
- Owner: currocam
- License: mit
- Language: Julia
- Default Branch: main
- Homepage: https://currocam.github.io/IdentityByDescentDispersal.jl/
- Size: 16.1 MB
Statistics
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
- Releases: 3
Metadata Files
README.md
IdentityByDescentDispersal
Getting started
This package provides an efficient implementation of the inference scheme proposed by H. Ringbauer, G. Coop and N. H. Barton (2017) to estimate the mean dispersal rate and the effective population density of a population.
The package is implemented in the Julia programming language and designed to be used from within a julia session. It integrates seamlessly with other statistical libraries in the julia ecosystem such as Turing.jl. However, we also provide an automated Snakemake pipeline to perform a complete analysis: from detecting and post-processing IBD blocks to finding a preliminary maximum likelihood estimate.
You can install this package by running:
julia
import Pkg
Pkg.add("IdentityByDescentDispersal")
This package provides the building blocks for performing likelihood-based inference of effective population densities and mean effective dispersal rates. Together with Turing.jl, it offers a flexible interface for fitting Bayesian or maximum-likelihood models.
julia
using IdentityByDescentDispersal
using CSV, DataFrames, Turing, StatsPlots
df = CSV.read("ibd_dispersal_data.csv", DataFrame)
contig_lengths = [1.0] # in Morgans
@model function constant_density(df, contig_lengths)
D ~ LogNormal(1, 1) # Effective population density
σ ~ Exponential(1) # Mean effective dispersal rate
# ⬇️ Composite log-likelihood function provided by IdentityByDescentDispersal.jl
Turing.@addlogprob! composite_loglikelihood_constant_density(D, σ, df, contig_lengths)
end
chain = sample(m, NUTS(), 1000)
Please refer to the documentation of the package for a detailed description of its functionality and recommended usage.
Simulation of synthetic datasets plays a major role in statistical inference and model validation. In addition to the inference machinery, this package also provides a set of recipes for developing advanced forward-in-time population genetics simulations in a continuous space. More information can be found in the simulations subdirectory.
Community Guidelines
IdentityByDescentDispersal.jl is an open-source project and contributions are welcome. Users are encouraged to report bugs, request features, or ask questions by opening a GitHub issue.
JOSS Publication
IdentityByDescentDispersal.jl: Inferring dispersal rates with identity-by-descent blocks
Authors
University of Antwerp, Belgium
University of Antwerp, Belgium
University of Antwerp, Belgium
University of Antwerp, Belgium, Naturalis Biodiversity Center, Leiden, Netherlands
Editor
Evan Spotte-Smith
Tags
population-genetics IBD dispersal spatial-genetics coalescent-theoryCitation (CITATION.cff)
cff-version: 1.2.0
title: IdentityByDescentDispersal.jl v1.0.0
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Francisco
family-names: Campuzano Jiménez
affiliation: 'University of Antwerp, Belgium'
orcid: 'https://orcid.org/0000-0001-8285-9318'
email: curro.campuzanojimenez@uantwerpen.be
- given-names: Arthur
family-names: Zwaenepoel
affiliation: 'University of Antwerp, Belgium'
orcid: 'https://orcid.org/0000-0003-1085-2912'
- given-names: 'Els Lea R '
family-names: De Keyzer
orcid: 'https://orcid.org/0000-0003-0924-0118'
affiliation: 'University of Antwerp, Belgium'
- given-names: Hannes
family-names: Svardal
affiliation: >-
University of Antwerp, Belgium and Naturalis
Biodiversity Center, Leiden, Netherlands
orcid: 'https://orcid.org/0000-0001-7866-7313'
repository-code: 'https://github.com/currocam/IdentityByDescentDispersal.jl'
url: >-
https://currocam.github.io/IdentityByDescentDispersal.jl/dev/
license: MIT
version: v1.0.0
date-released: '2026-02-13'
GitHub Events
Total
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- Push event: 82
Last Year
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- Issue comment event: 3
- Push event: 82
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| Name | Commits | |
|---|---|---|
| Curro Campuzano | c****m | 34 |
| Curro Campuzano | 6****m | 15 |
| github-actions[bot] | 4****] | 11 |
| dependabot[bot] | 4****] | 5 |
| Arthur Zwaenepoel | a****l@g****m | 1 |
| CompatHelper Julia | c****y@j****g | 1 |
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Last synced: 9 days ago
All Time
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- Average comments per issue: 4.33
- Average comments per pull request: 0.0
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Past Year
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- Pull requests: 16
- Average time to close issues: 3 days
- Average time to close pull requests: 5 days
- Issue authors: 2
- Pull request authors: 4
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- Average comments per pull request: 0.0
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 12
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Total downloads:
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- Total versions: 6
juliahub.com: IdentityByDescentDispersal
Efficient estimation of effective densities and dispersal rates using identity-by-descent blocks
- Homepage: https://currocam.github.io/IdentityByDescentDispersal.jl/
- Documentation: https://docs.juliahub.com/General/IdentityByDescentDispersal/stable/
- License: MIT
-
Latest release: 1.0.0
published about 2 months ago
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